ΦClust: Pheromone-based Aggregation for Robotic Swarms
Bibliographic record
Abstract
on Intelligent Robots and Systems (IROS 2018), held for the first time in Spain, in the lively capital city of Madrid.Nowadays Madrid is a cosmopolitan open city that combines the most modern infrastructures and the status as an economic, financial and administrative center of Southern Europe, with a large cultural and artistic heritage, a legacy of centuries of exciting history.Art, traditions, gastronomy and multicultural openness play a key role in Madrid's life.This year IROS has broken all records, becoming the biggest scientific robotic conference ever.We received a total of 2,700 paper submissions from 62 countries, including 2,197 regular paper submissions and 503 submissions to Robotics and Automation Letters (RA-L) with IROS option.After more than 20,000 reviews (IROS and RA-L), a total of 1,254 papers were accepted into the IROS program, representing an acceptance rate of 46%.A total of 84 workshop and 15 tutorial proposals were submitted, and 48 workshops and 8 tutorials were accepted, which will take place on the two days before and after the main conference.More than 3,300 researchers and industrial partners have registered for the full conference and more than 2,200 have registered for workshops and tutorials.This tremendous growth confirms the IROS 2018 motto -"Towards a robotic society".Looking at these record numbers, it is also interesting to notice that the distribution of accepted papers among world regions is: 25% from Asia and Australia/Oceania, 29% from America and 46% from Europe.The European Union (EU) countries contribute with a 41%, being a leading robotic community as a direct result of the individual countries and EU investments in robotics.The EU's Horizon2020 research program emphasizes in robotics, AI and cognitive systems.Besides, the euRobotics association plays a crucial role in this leadership.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".